SUMMARY Nowadays, it is common to find problems that require recognizing objects in an image, tracking them along time, or recognizing a complete real‐world scene. One of the most known and used algorithms to solve these problems is theSpeeded Up Robust Features (SURF) algorithm.SURFis a fast and robust local, scale and rotation invariant, features detector. This means that it can be used for detecting and describing a set ofpoints of interest(keypoints) from an image. Because of the importance of this algorithm and the rise of the parallelism‐based technologies, in the last years, diverse parallel implementations of SURF have been proposed. These parallel implementations are based on very different techniques: Compute Unified Device Architecture, OpenMp, OpenCL, and so on. In conclusion, we think valuable a comparative study of all of them highlighting the advantages and disadvantages of each parallel implementation. To our best knowledge, this article is the first attempt to do this comparative study. In order to make this study, we have used the standard metrics and image collection in this field, as well as other important metrics in parallelism as speedup and efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

This paper describes a real-time method that obtains a hybrid description of the environment (both metric and semantic) from raw data perceived by a 2D laser scanner. A set of linguistically labelled polylines allows to build a compact geometrical representation of the indoor location where a set of representative points (or features) are semantically described. These features are processed in order to find a list of traversable segments whose middle points are heuristically clustered. Finally, a set of safe paths are calculated from these clusters. Both the environment representation and the safe paths can be used by a controller to carry out navigation and exploration tasks. The method has been successfully tested in simulation and on a real robot.

This paper presents a probabilistic framework where uncertainties can be considered in the mosaic building process. It is shown how can be used as an environment representation for an aerial robot. The inter-image relations are modeled by homographies. The paper shows a robust method to compute them in the case of quasi-planar scenes, and also how to estimate the uncertainties in these local image relations. Moreover, the paper describes how, when a loop is present in the sequence of images, the accumulated drift can be compensated and propagated to the rest of the mosaic. In addition, the relations among images in the mosaic can be used, under certain assumptions, to localize the robot.

The paper presents a vision-based position estimation method for UAVs. It assumes a planar scene, approximation that usually holds when a vehicle is flying at a relatively high altitude. Monocular image sequences gathered by the UAV are used to estimate the vehicle motion, but accumulative errors can make diverge the estimated position. The proposed method uses an online-built mosaic to correct the drift associated to the planar motion estimation algorithm. The mosaic allows to use not only the current image but also previously recorded information for localization. Results from actual field experiments are presented

This paper presents a vision-based method to estimate the * real motion of a single camera from views of a planar patch. Projective techniques allow to estimate camera motion from pixel space apparent motion without explicit 3-D reconstruction. In addition, the paper will present the HELINSPEC project, the framework where the proposed method has been tested, and will detail some applications in external building inspection that make use of the proposed techniques.

This work presents real time computer vision techniques for autonomous navigation and operation of unmanned aerial vehicles. The proposed techniques are based on image feature matching and projective methods. Particularly, the paper presents the application to helicopter motion compensation and object detection. These techniques have been implemented in the framework of the COMETS multi-UAV systems. Furthermore, The work presents the application of the proposed techniques in a forest fire scenario in which the COMETS system can be demonstrated.

This paper describes a method for mobile robot position estimation based on feature tracking through the sequence of images provided by a conventional video camera. The block-based correspondence algorithms selects candidates through grey level matching and local coherence analysis; a DSP-based multiprocessing image processing system is used to achieve real time performance. Point-to-point correspondences are used to estimate the motion parameters of a mobile robot. Motion-based procedures to eliminate false matching have been implemented. The method has been applied to a mobile robot in non-structured and outdoor environments.